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20162022
most citedScaling Up Models and Data with and

48 citations · 129 across the 4 of their papers we have counts for

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9 papers · 1 filter

cs.CL2020

QED: A Framework and Dataset for Explanations in Question Answering

Matthew Lamm, Jennimaria Palomaki, Chris Alberti +4

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggab…

cs.CL2019

Measuring Domain Portability and ErrorPropagation in Biomedical QA

Stefan Hosein, Daniel Andor, Ryan McDonald

In this work we present Google's submission to the BioASQ 7 biomedical question answering (QA) task (specifically Task 7b, Phase B). The core of our systems are based on BERT QA mo…

cs.CL2019

Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension

Daniel Andor, Luheng He, Kenton Lee +1

Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers. We e…

cs.CL201912 cited

Synthetic QA Corpora Generation with Roundtrip Consistency

Chris Alberti, Daniel Andor, Emily Pitler +2

We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ens…

cs.CL2018

Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings

Bernd Bohnet, Ryan McDonald, Goncalo Simoes +3

The rise of neural networks, and particularly recurrent neural networks, has produced significant advances in part-of-speech tagging accuracy. One characteristic common among these…

cs.CL2018

Linguistically-Informed Self-Attention for Semantic Role Labeling

Emma Strubell, Patrick Verga, Daniel Andor +2

Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dram…